Backpropagation is a main method for training neural networks in which the gradient of the loss function is computed with respect to the weights. This gradient is propagated backward through the network and the new weights are obtained by a variant of gradient descent algorithm. The hyper-parameters of gradient descent with momentum including decay rate and learning rate are included in backpropagation algorithm and they are optimized together with neural network weights in [1]}.
